A company wants to create a chatbot that answers questions about human resources policies.

The company is building a chatbot using an LLM to answer questions about HR policies, with access to a large digital documentation base. Retrieval Augmented Generation (RAG) optimizes the LLM’s responses by retrieving relevant information from the documentation base and using it to generate accurate, contextually grounded answers, reducing hallucinations and improving response quality.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Retrieval Augmented Generation (RAG) enhances the performance of large language models by retrieving relevant information from external knowledge bases, such as documentation or databases, and incorporating it into the generation process. This technique ensures responses are accurate and grounded in the provided data, making it ideal for applications like policy chatbots."

(Source: AWS Bedrock User Guide, Retrieval Augmented Generation)

Detailed Explanation:

Option A: Use Retrieval Augmented Generation (RAG).This is the correct answer. RAG leverages the documentation base to provide the LLM with relevant HR policy information, optimizing the chatbot’s responses for accuracy and relevance.

Option B: Use few-shot prompting.Few-shot prompting provides a few examples in the prompt to guide the LLM, but it is less effective than RAG for large documentation bases, as it cannot dynamically retrieve specific policy details.

Option C: Set the temperature to 1.Setting the temperature to 1 controls the randomness of the LLM’s output but does not optimize responses using external documentation. This option is unrelated to the documentation base.

Option D: Decrease the token size.Decreasing token size (likely referring to limiting input/output tokens) may reduce response length but does not optimize the quality of responses using the documentation base.